23 citations · 67 across the 4 of their papers we have counts for
4 papers
Nonlinear Spatial Filtering in Multichannel Speech Enhancement
Kristina Tesch, Timo Gerkmann
The majority of multichannel speech enhancement algorithms are two-step procedures that first apply a linear spatial filter, a so-called beamformer, and combine it with a single-ch…
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement
Robert Rehr, Timo Gerkmann
In this paper, we address the generalization of deep neural network (DNN) based speech enhancement to unseen noise conditions for the case that training data is limited in size and…
Normalized Features for Improving the Generalization of DNN Based Speech Enhancement
Robert Rehr, Timo Gerkmann
Enhancing noisy speech is an important task to restore its quality and to improve its intelligibility. In traditional non-machine-learning (ML) based approaches the parameters requ…
On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement
Robert Rehr, Timo Gerkmann
For enhancing noisy signals, machine-learning based single-channel speech enhancement schemes exploit prior knowledge about typical speech spectral structures. To ensure a good gen…